Medical diagnostic ultrasound imaging methods for extended field of view

ABSTRACT

A medical diagnostic ultrasound imaging system aligns substantially co-planar two-dimensional images to form an extended field of view using improved compounding methods. Compounding with a finite impulse response is used for more versatile compositing. The compounding is adaptive, such as through adapting the image regions, weighting, or type of compounding as a function of correlation, location within the image, estimated motion or combinations thereof. A user warning is provided as a function of the correlation between images.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of copending U.S. patentapplication Ser. No. 09/384,707, filed Aug. 26, 1999 which is acontinuation-in-part of Ser. No. 09/196,986, filed Nov. 20, 1998, whichare hereby incorporated by reference in their entirety.

BACKGROUND

The present invention relates to medical diagnostic ultrasonic imaging,and in particular to improved methods used in connection with thecombination of two or more partially overlapping images into an extendedfield of view image.

Hossack et al. in U.S. Pat. No. 6,014,473, filed Aug. 22, 1997, assignedto the assignee of the present invention and hereby incorporated byreference in its entirety, describe systems for acquiring, aligning andregistering multiple medical diagnostic ultrasound images. Suchalignment is used both to determine the motion between two selectedimages as well as to provide the information needed to composite anextended image from two or more selected ultrasound images. That is, twocoplanar tracking images can be aligned and in this way the relativemotion of the transducer between the times of the two tracking imagescan be obtained. Similarly, two or more substantially coplanardiagnostic images can be aligned and then composited to form an extendedfield of view.

In one embodiment, Hossack et al. disclose adaptively determining thenumber of image data frames that are collected between consecutivetracking frames (column 22, lines 18-50). The number of image dataframes collected between tracking frames varies in response to theestimate of motion between each image data frame.

The collected frames are compounded. The compositing method described inU.S. Pat. No. 6,014,473 interpolates data from boundary portions ofdifferent image data frames with variable weights (column 29, lines5-22). The weights applied to the image data frames vary linearly as afunction of distance where the weights for one frame are one minus theweights for the other frame. Using these weights, a previous frame iscompounded with a subsequent frame. The composition of the previousframe is not discussed, but Hossack et al. note that compounding can beused for accumulating image data.

Likewise, Weng et al. in U.S. Pat. No. 5,575,286 disclose a “rampcompounding” which gives weight ramps for both a new image frame and anexisting compound image in the overlapping area (column 8, lines 21-30).Weng et al. do not discuss the composition of the existing compoundimage. Weng et al. also disclose alternatives to the ramp compounding,such as using only new pixels for non-overlapping regions or recursivelyaveraging the new image frame with the existing compound image.

The present invention is directed in part to an improved compoundingmethod that provides versatility and is quick to execute.

SUMMARY

By way of introduction, the preferred embodiments described belowprovide methods and systems for combining multiple frames of data for anextended field of view. Using finite-impulse response combinations,adaptive compounding or combinations thereof, the amount of speckle isreduced, and/or the signal-to-noise ratio is increased for the extendedfield of view images.

The present invention is defined by the following claims. This paragraphhas been provided merely by way of introduction, and is not intended todefine the scope of the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of a method that incorporates a presentlypreferred embodiment of this invention.

FIG. 2 is a schematic diagram of a parent image and a test blocksuitable for use in the method of FIG. 1.

FIG. 3 is a schematic diagram of an extended field of view generated bythe method of FIG. 1.

FIGS. 4 and 5 are diagrammatic views of two different test blockssuitable for use in the method of FIG. 1.

FIGS. 6-8 are diagrammatic views of test blocks rotated by 0, 1 and 2pixels, respectively.

FIGS. 9a and 9 b combine to form a flow chart of another preferredembodiment of the method of this invention.

FIG. 10 is a flow chart of another preferred embodiment of the method ofthis invention.

FIGS. 11 and 12 are diagrammatic views showing the spatial relationshipof B-mode and color Doppler regions in a parent image.

FIG. 13 is a flow chart of another preferred embodiment of the method ofthis invention.

FIG. 14 is a diagrammatic view showing one embodiment of a compoundingmask.

FIG. 15 is a diagrammatic view showing another embodiment of acompounding mask.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Various methods for estimating motion between two or more componentimages for forming an extended field of view image may be used. Some ofthese methods are discussed below. After an introduction to these motionestimation methods, compounding methods are discussed.

Turning now to the drawings, FIG. 1 shows a block diagram of oneimplementation of a method for estimating motion between two images. Ina first step 10 at least two medical diagnostic ultrasound images areacquired, using any suitable technology. For example, the Sequoia orAspen systems of Acuson Corporation can be used to acquire these twoimages in any suitable manner. As discussed in greater detail below,these images may be in any desired imaging mode, including B-mode, ColorDoppler mode, and fundamental mode or harmonic mode (including harmonicimaging of contrast agent and harmonic imaging of tissue without addedcontrast agent). On occasion, these originally acquired images will bereferred to in this specification as parent images.

The two images are preferably substantially coplanar and partiallyoverlapping, and can be obtained as separate frames acquired as atransducer probe is translated and potentially rotated in the XZ plane.The widest variety of transducer probes can be used, includingconventional external probes as well as intracavity probes, such astransducer probes designed for insertion into the esophagus or otherbody cavities, intravascular and other catheter-based transducer probes,and intraoperative transducer probes (transducer probes designed for useduring surgical operations in direct contact with internal organs).

In step 12, test blocks are selected from the images acquired in step10. FIG. 2 shows one example, in which a test block 22 is shown as acentral strip taken from a parent image 20. As described below, the testblock 22 can be shaped as desired, and various techniques can be used tomodify the test block as compared to the parent image. For example, thetest block can be reduced in complexity by various filtering,decimation, and other techniques as described below. FIG. 2 shows theconventional X (azimuthal) and Z (range) dimensions as they relate tothe parent image and the test block 22.

Returning to FIG. 1, the next step 14 is to find the translation in theXZ plane that matches the test blocks from the two images. As explainedbelow, the match can be measured in many ways, including the minimum sumof absolute differences (MSAD) technique and various correlationtechniques that utilize multiplications. Note that in step 14 the testblocks are merely translated with respect to one another and are notrotated. As described below, the location, range, and orientation of thetranslations that are used in step 14 can be selected adaptively tominimize the search time.

In step 16, the test blocks are aligned in translation using thematching translation of the preceding step and then rotated about acentral axis 24 (FIG. 2) to find the angle of rotation that best matchesthe test blocks. As before, the angles of rotation that are searched canbe selected adaptively based on previous searches to speed the search.

In the method of FIG. 1, the matching translation and rotation angle arefound independently. This offers an advantage over a method that detectsmultiple translations from among several blocks and infers the rotationsubsequently. In practice, motion may be irregular in either, or both,translation and rotation in a completely independent fashion. Typically,translation errors are of lesser consequence since they result in lessserious cumulative geometric errors. Angular errors can have a moreserious impact since the errors tend to accumulate and an angular errorresults in increasing translational errors in subsequently acquiredframes. Therefore, it is preferable to independently control the searchrange for valid angular motions between frames.

Finally, in step 18 the matching translation and rotation from theprevious steps are used as registration information to composite atleast parts of the original parent images. FIG. 3 shows one example inwhich two parent images 20, 20′ are composited with one another. Theparent images 20, 20′ overlap in an overlapping region 20″, and theregistration information (ΔX, ΔZ, Δθ) is used to align the parent images20, 20′.

The method of FIG. 1 preferably takes advantage of the nature of realmotion of a transducer over the tissue. Typically, translation along theX (azimuth) axis is far greater than translation along the Z (range)axis; the transducer slides along the surface and is not pressed intothe tissue. Rotation effects are generally relatively modest compared totranslation effects. This will certainly be true for the most commonapplications—such as scanning the abdominal surface or scanning alongnecks or legs.

The test block shape is preferably optimized to facilitate rotationdetermination. The block is made narrow in the azimuthal direction (e.g.32 pixels along the X axis) and long in the range direction (e.g. 320pixels—or almost the entire available image depth along the Z axis). Thereason for this is that as the block is rotated by small amounts, theeffect of rotation can be approximated by translating rows of pixels inthe top and bottom regions of the test block. If the pixel block weresquare, one would have to translate the pixels in both X and Z, sincethe motion in the Z direction would be non-negligible.

FIGS. 4 and 5 illustrate this effect. In FIG. 4 the test block 22 isrotated by an angle θ. To first order, the motion of each row of pixelsalong the X-axis is simply translation along the X-axis, and none of thepixels of the test block 22 moves substantially in the Z direction. Incontrast, when a wider test block 22′ is used as shown in FIG. 5, acomparable rotation through the angle θ produces substantial motionalong the Z-axis for pixels near the corners of the test block 22′. Forthis reason, the ratio of the range extent to the azimuthal extent ofthe test block is preferably greater than 5, more preferably greaterthan 9, and in the embodiments described below substantially equal to10.

The search of step 14 is typically primarily in the azimuthal direction,but the search can also contain a small search (a few pixels) in therange direction. The search is preferably conducted firstly in theazimuthal direction only, followed by a fine scale, two-dimensionalsearch in both X and Z.

The search of step 16 is most efficiently performed by generating apixel offset table for each angular search, since high accuracy and highspeed are both important. Also, small rotations are far more likely thanlarge rotations. Therefore the rotations are typically in terms ofsingle pixel motion increments at the extremes (top and bottom) of thetest block.

Preferably, a pixel offset table is generated once when the program isstarted. A predefined angular search range is defined, e.g.−5 to+5degrees in 0.2 degree steps. For every angular step one increments downthe pixel block in the range direction and calculates the associatedazimuthal (X) direction offset for the particular angle and pixellocation in the range direction. The pixel offset is defined by thefollowing equation:

Round((pixel_index*tan(theta)),

where Round is a function returning the nearest whole number,pixel_index is the index of the pixel in the range direction withrespect to the center of the text block (i.e. the axis of rotation), andtheta is the particular angle. In this example, pixels are assumed to besquare. If the pixels are not square, the above equation is modified tocorrect for the pixel aspect ratio, as for example as follows:${Round}\quad {\left( {\left( \frac{pixelwidth}{pixelheight} \right)\quad {pixel\_ index}*\tan \quad ({theta})} \right).}$

Consider as an example, an image which extends over 301 pixels in therange direction. The center pixel in the range direction is 151.Consider a pixel 10 pixels down in the range direction and a selectedangle equal to +0.5 degrees. The pixel offset entry for this pixel depthand selected angle is:

Round ((151−10)*tan (0.5*pi/180)=Round (1.23)=1

This process is repeated for all pixel depths and rotation angles tofill the pixel offset table, and the process is performed only once perprogram run.

FIGS. 6, 7 and 8 illustrate three such pixel offset tables in simplifiedform. FIG. 6 shows a ten-pixel offset table appropriate for zerorotation. As shown at 26, the rotated test block is identical to theoriginal test block. FIG. 7 shows an offset table for a rotation of onepixel in a clockwise direction about the axis 24. The motion ranges from+1 pixel to −1 pixel over the pixels 1 through 10, and the rotated testblock is shown at 26′. FIG. 8 shows an offset table appropriate forrotation of 2 pixels in a clockwise direction, and the motion plotted inthe table ranges from ±2. The rotated test block 26″ shows the manner inwhich pixels are translated along the X direction, without translationalong the Z direction. Similar tables can be created for anti-clockwiserotations.

For both steps 14 and 16 of FIG. 1, it is beneficial to calculate themotion to sub-step-size accuracy for both translation and rotation. Thiscan be done by fitting a quadratic to the values around the MSADminimum. Having found the equation for the fitting quadratic, the trueminimum to sub-step-size accuracy is determined from the zero in thequadratic equation after differentiating. In this specification stepsize corresponds to the pixel search resolution for translation and theangular step resolution for rotation.

Once the matching translation and rotation are determined, the motionsof all points along the central axis of the sub-block are known. If oneconsiders the rotation as an indication of slope and the translation asa constant, we have the essential components for defining motion interms of y=mx+c where c is the translation amount, m is the slope or tan(angle of rotation), and in this case x is the range component and y isthe translation amount at that range along the X or azimuthal axis. Inthis way, the motions of all points on the plane may be found in orderto move one image into registration with another.

The extended field of view processing described above can be performedimmediately after image acquisition and displayed on the ultrasounddisplay of the acquisition system. Alternately, such extended field ofview processing can be performed after the fact on a remote computerusing stored image data.

Further details regarding preferred and alternative implementations ofthe method described above and related methods are set out below,including methods for estimating motion and methods for compounding twoor more component images to form an extended field of view of image.

1. An iterative search can be used as set out below and as shown in FIG.9.

Process:

(a) In step 30, search first for a match in terms of translation (eitherX only or X and Z).

(b) In step 32, search for match in terms of rotation, taking account ofthe translation found in step (a).

(c) Taking account of the match from steps 30 and 32, determine in step34 if a translation (X and/or Z) can be found that provides a bettermatch.

(d) Taking account of the match for translation and rotation obtainedfrom steps 30, 32 and 34, search in step 36 for a rotation that providesa better match.

(e) Repeat steps 34 and 36 using updated estimates of translation androtation.

It steps 30, 32, 34 and 36, it is preferred to find a best match in eachcase. Alternately, in each of these steps a high speed search can beused that provides a better match, though the search is too fast orapproximate to find the best match. As used herein, the term “match” isintended broadly to encompass best matches as well as improved matches.

The number of times steps 34 and 36 are repeated can be determined invarious ways. In the illustrated embodiment, M (the number of timessteps 34 and 36 have been executed) is compared with a constant, equalto 2 in this embodiment. The constant can be greater if desired.Alternately, steps 34 and 36 can be repeated until a measure of thequality of the match (such as the ratio of the minimum SAD to the meanSAD) reaches some predetermined acceptable level, or alternately until alarger number of repetitions has been completed.

By way of example, the test block width may be set to 16×N, where N is asmall integer. For N=2, this results in a width in the azimuthaldirection of 32. The first translation matching step 30 of the iterativemethod of FIG. 9 can be in the X direction only, e.g. over a range of±32 pixels. The search region may be varied adaptively based on previousestimates of motion in the X direction, and the range may be made lessthan ±32 pixels. The first rotation matching step 32 of the iterativemethod of FIG. 3 can be over a range of ±3 degrees. The secondtranslation matching step 34 can be over a range of ±2 pixels in the Xdirection and ±1 pixels in the Z direction. The second rotation matchingstep 36 can be over a range of ±0.5 degrees.

2. The test block need not be tall and narrow, but as described above,such a shape for the test block provides advantages.

Consider a rectangular test block 30 pixels wide and 300 pixels high.Assume individual pixels are square. For this test block, for rotationsup to 2.42 degrees there is no requirement for any pixel to shift in theZ direction by more than 0.5 pixels. Therefore, for rotations up to 2.42degrees Z motion calculations can be ignored. Being able to ignore Zmotion calculations has two advantages:

(a) Z motion calculations are not required; this saves computation time.

(b) Pixel rows in the reference frame (old frame) and the test positionframe (new frame) both occupy consecutive memory locations (assumingpixels are ordered along rows in memory, that is, as a 2D array is read,pixel address first increments in X, followed by increments in Y aftereach row has been read). This ordering makes the programming simpler andfaster. In particular it greatly facilitates the use of pointer-basedprogramming and also makes the manipulations of blocks of pixels inparallel fashion far more tractable. For example, using Intel Pentium™microprocessors with MMX™, this approach allows for manipulation ofeight 8-bit binary numbers in parallel. The fact that pixels are groupedcontinuously in rows in both the old and the new frames facilitateseasier implementation.

In contrast, if the pixel block is 300 pixels wide by 300 pixels high,then rotations of more than 0.19 degrees result in Z motions of morethan 0.5 pixels in at least some pixels. Experience indicates thatrotational motions between successive frames are frequently greater than0.19 degrees but rarely greater than 2.42 degrees. In cases where Zmotion is greater than 0.5 pixels, then it is still quite possible toreassign pixels from the Z shifted locations to the new frame pixelblock so that an accurate rotation motion calculation can be performed.

3. Color Doppler ultrasound image data (Velocity, Energy orcombinations) may form part or all of the images used with this method.If both B-mode data and Color data are available for display, then theB-mode data for the entire pixel block can be used to determine imageregistration information, and the motion calculated for a particularB-mode region can also be applied to the related (superimposed) Colorregion. Color data, such as velocity or energy data, may also becompounded as discussed below to form the extended field of view image.

4. Fundamental or harmonic data may form part or all of the images usedwith this method.

5. Image data can be pre-processed prior to motion estimation andpost-processed after composition (image registration). Examples includethe following:

(a) Gray scale remapping, i.e. the as-acquired 0-255 range of gray scalevalues is remapped using a non straight line function.

(b) As an example of the above, contrast enhancement (e.g. histogramequalization) is used. As another example, 24 bit RGB data is decimatedto 8 bit gray data.

(c) Pixel contrast resolution is decimated to speed up motionestimation, e.g. 0-255 pixel levels are mapped to 0-15, or even to 0-1(i.e. only two binary levels) prior to motion estimation.

(d) Image brightness is altered. The resultant image is passed through a0-255 mapping function that increases the mean pixel level (but clipsthe upper end at 255 to prevent wrapping beyond 255 and back to lowinteger numbers (associated with near black)).

(e) Image data is filtered in some way (e.g. low-pass filtered).

(f) The output image is speckle reduced (e.g. low-pass filtered).

6. The preferred motion merit function is typically MSAD (Minimum Sum ofAbsolute Differences); however, other matching techniques, such asfinding the matching translation/rotation using cross-correlation (sumof multiplies) techniques may also be used.

7. Sub-pixel estimation can be applied to translation, andsub-angular-step-size estimation can be applied to rotationcalculations.

As an example, the levels of the sum of absolute differences (SAD) forthe minimum and its neighbors are compared to determine the likelyposition of the minimum to sub-pixel resolution. A simple way to do thisis to fit a quadratic equation (y=ax Ô2+bx+c) to three points (the pixely2 with the minimum SAD plus the next-neighboring pixels y1 and y3 oneach side). The x values for these three data points are x1, x2 and x3,and these x values are typically separated by unity (the pixel spacingfor translation searches and the angular step size for rotationsearches). Data for three points (x1, y1), (x2, y2), x3, y3) are solvedsimultaneously for the equation y=ax Ô2+bx+c. In this way a, b and c (cnot strictly needed) are found. The derived quadratic equation isdifferentiated and set equal to zero to solve for:

2ax+b=0.

In this way, the value of x to sub-step-size resolution is found.

8. The pixel data used in the test block may be pre-scan-conversionacoustic line data or post-scan-conversion video data. The pixel datafor the test block can be envelope detected, raw RF, baseband quadrature(I,Q), ideally processed to give coherency between adjacent lines asdescribed in Wright U.S. Pat. No. 5,623,928, assigned to the assignee ofthe present invention. Likewise, the pixel data used for compositing theimages may comprise one or more of these variations.

9. The images used to create the test blocks may be acquired at the rateof one receive line per transmit line or at the rate of multiple receivelines per transmit line as described in Wright U.S. Pat. No. 5,685,308,assigned to the assignee of the present invention.

10. As described in U.S. Pat. No. 6,014,473, poor estimates of motioncan be identified. A poor estimate of motion may be determined based ona comparison of the level of the MSAD compared with the mean SAD. Also,a poor estimate of motion is often associated with a dissimilaritybetween the present estimate and previous estimates. As described inU.S. Pat. No. 6,014,473, these various available confidence factors canbe combined (e.g. using fuzzy logic) or used independently (e.g. if agiven MSAD is unacceptable, a previous frame-to-frame motion estimatecan be used).

As one example, the MSAD can be compared with a threshold to determineif the corresponding estimate of motion is reliable. One suitablethreshold is equal to the number of pixels in the test block multipliedby a constant, such as 5 for example. In the event the MSAD for aparticular estimate of motion is less than the threshold and istherefore indicative of a reliable estimate of motion, then thecalculated estimates of motion (i.e. ΔX, ΔZ, and Δθ) are used and threevariables are updated as follows:

history_ΔX=(history_ΔX+3*ΔX)/4,

history_ΔZ=(history_ΔZ+3*ΔZ)/4,

history_Δθ=(history_(—)Δθ+3*Δθ)/4.

The weights used in the weighted averages of the foregoing equations aremerely examples of possible choices. Other alternatives can be used. Inthe event the MSAD associated with a particular estimate of motion isgreater than the threshold, and therefore indicative of an unreliableestimate of motion, then the history variables are not updated and theestimated motion is replaced with values that vary predominantly withthe history variables discussed above according to the followingequations:

ΔX=(3*history_ΔX+ΔX )/4,

ΔZ=(3*history_ΔZ+ΔZ)/4,

Δθ=(3*history_Δθ+Δθ)/4.

Once again, the weights used in the weighted averages are only examples.

11. Frames being compared need not be consecutive. Instead of everyframe being used, every Nth frame can be used (N=2,3 etc.).

12. Frames being registered need not be consecutive. Instead of everyframe being used, every Mth frame can be used (M=2,3 etc.).

13. If every Nth frame is being used for motion estimation (N=2,3 etc.)then every Mth frame (M not necessarily equal N) can be used forregistration or rendering.

14. The rendered image regions that are composited to form the extendedfield of view need not necessarily comprise the entirety of therespective image. Preferably only a subset of the image is composited,such as an image block extending over the full Z dimension and over awidth in the X direction that is slightly wider than the search area.Preferably, the central region of the image is composited, since itgenerally has the best resolution.

15. The compositing method preferably includes the use of texturemapping, as found in the OpenGL Applications Programming Interface. SeeOpenGL Programming Guide (M. Woo et al.) published by Addison Wesley(1997). OpenGL is a trademark of Silicon Graphics.

16. A visible or acoustic warning is preferably given to the user inevent of a poor motion estimate or a series of poor motion estimates, asdescribed in U.S. Pat. No. 6,014,473. Such a warning can indicate to auser that a re-scan is required.

17. A user-visible icon (e.g. a variable length arrow) is preferablyused to guide the user to make a scan at an optimal speed, as describedin U.S. Pat. No. 6,014,473.

18. The registration method can be used in combination with ECG and/orrespiratory triggering/gating. (Triggering is when acquisition istriggered by ECG or other events. Gating is when potential triggeringevents are analyzed to see if they are valid, i.e. they fit somepredefined criteria.) For example, the selected images for registrationalignment may be only those that are acquired in response to a selectedtrigger.

When forming the extended field of view of a pulsing object (e.g., anartery such as the carotid artery), it may be preferable to acquire onlyimages during a particular portion of the cardiac cycle—such as endsystole or some time interval after observed end systole. It is wellknown in ultrasound imaging to acquire an ECG signal using 3 ECGelectrodes connected to the chest of the individual. The ECG signalexhibits some easily recognized peaks, such as the R wave. The R waveoccurrence can be observed using an electronic trigger set to fire aftereach ECG voltage pulse surpassing some preset threshold is detected.This detected electronic trigger pulse can have a user-selectable delayapplied to it and the resulting delayed electronic trigger used totrigger the start of an ultrasound image frame acquisition. Hence,ultrasound image frames are acquired once per heart cycle and atpresetable delays from the detected R wave. It is also possible to use agating technique wherein successive detected trigger pulses are comparedwith previously detected pulses and simple computer logic used todetermine whether the ECG signal is gated or not. Typically, an ECGsignal is gated only if the detected R wave interval falls withincertain pre-programmed valid bounds. For example, suppose the R wavesare occurring at 1000 mS intervals. A range of valid R waves can be setso that only R wave intervals in the range 900-1100 mS are gated. If oneR wave signal is at 800 mS from the previous R wave, then it isassociated with an irregular heart beat and ignored (since it may causean image artifact due to irregular motion). This technique is discussedin McCann “Multidimensional Ultrasonic Imaging for Cardiology” Proc.IEEE Vol. 76, No. 9, September 1998, pp. 1063-1073.

19. During image composition, pixel values are preferably interpolatedor averaged based on the pixel levels in the overlapping regions of thetwo registered frames, as described in U.S. Pat. No. 6,014,473.

20. The images are preferably ultrasonic medical diagnostic imagesacquired with 1 D arrays employing a fixed focus in the elevationdirection, one or more fixed foci in azimuth (transmit) and dynamicreceive focus in azimuth. Ideally, a 1.5D array is employed that isfocused in elevation, typically using a fixed transmit focus and adynamically updated receive focus. Acuson Piano-Concave transducers asdescribed in the related Hanafy U.S. Pat. No. 5,651,365, assigned to theassignee of this invention, can also be used.

Any suitable technology can be used for the transducer array, includingpiezoelectric ceramics (e.g. PZT), piezoelectric polymers (e.g. PVDF),relaxor ferroelectric ceramics (e.g. PMN-PT), and electrostatictransducers. Electrostatic transducers are described in “MicromachinedCapacitive Transducer Arrays for Medical Ultrasound Imaging,” X. C. Jinet al, Ultrasonic Symposium (Sendai, Japan, Oct. 5-8, 1988). The presentinventors have recognized that such electrostatic transducers can beused in all conventional medical diagnostic ultrasonic imaging modes,including B-mode, Color Doppler mode, pulse wave mode, continuous wavemode, fundamental imaging mode, and harmonic imaging mode, with orwithout added contrast agent.

21. Registration and composition processing can be performed on-line (onthe ultrasound system processor) or can be done off-line (at anarbitrary time after acquisition). If performed off-line, datacompression such as JPEG compression can be used to speed data transfer.

22. The positioning of the search region for the test block can bevaried adaptively: if the previous optimal value was found at 5 pixelsto the right then the next search is preferably centered at 5 pixels tothe right. Further details can be found in U.S. Pat. No. 6,014,473.

For example, the current search can be centered on the offset estimatedfrom the previous search. This is reasonable since the user typicallyuses a smooth motion in which a rapid change of velocity betweensuccessive frames is not expected. Suppose the first search is over therange +/−20 pixels. This is a 41 pixel search. However, if the optimummatch (MSAD) is found at +10 pixels, then the subsequent search may beset to search from +5 pixels to +15 pixels (an 11 pixel search). In thisway the search area is minimized and the overall speed improved. As wellas changing the bias or center of the search, it is also preferred tovary the size of the search. If the successive searches areapproximately uniform (e.g., 8, 7, 8, 9, 7 pixels all to the right),then it may be preferable to search the range +6 to +10. Alternately, ifthe successive searches are semi-random (e.g., 5, 7, 9, 3 pixels, all tothe right), a better search range would be +1 to +15. Further, the sizeof the search can be made to vary according to the quality of theprevious searches. If the ratio of MSAD (minimum SAD) to mean SAD isclose to one, this means that the estimates are of poor quality, andthat a high degree of uncertainty exists. In this case, larger searchregions are preferred. Similar techniques can be applied to therotational search.

23. The size of the test block can be varied adaptively. A small testblock that yields good results is preferred because processing time islow. Otherwise, a bigger test block should be used to improve quality(e.g. the ratio of MSAD to mean SAD, or the similarity with respect toprevious estimates). The decision to increase or decrease the test blocksize can be made on the basis of any quality measure, including thosementioned above.

24. Image resolution in the test block can be decimated (e.g. use onlyevery Nth pixel in X and every Mth pixel in Z). The new pixel dimensionsare then taken into account when determining motion in real units (mm).Preferably the image is low-pass filtered prior to decimation.Non-decimated images can be used for compositing even if decimatedimages are used for motion estimation.

25. The size of the image motion test block can be adaptively alteredbased on lack of signal at depth. If the lower (deeper) part of the testblock is noisy (randomly varying levels—not like acoustic speckle whichfollows a pattern determined in part by the acoustic system point spreadfunction) or if the deeper range part of the image is black, there islack of good signal at greater depths, and the size of the test blockcan be reduced to ignore this region. For any given transducer andfrequency there is a range (which can be experimentally measured) beyondwhich the displayed signal is unreliable because the acoustic signal isapproximately the same size as or less than the electronic noisethreshold of the system. This range can be pre-computed for alltransducers and operating conditions or measured on test objects andthen stored in a look up table. Whenever the system defines a test blockto be used for motion estimation, it selects only pixels lying above(shallower) than the noise threshold region. An alternative methodinvolves measuring acquired acoustic line data and detecting randomlyvarying signals. This can be performed using cross-correlation ofsuccessive signals, similar to the method used for Doppler detection ofblood flow. If the lines are fired often enough, even if there is imagemotion the cross-correlation of signals will reveal a genuine detectablesignal in the near field and no correlation in regions dominated byelectronic noise.

26. If using harmonic data, the subject either may or may not containnon-linear scattering agent. Further the composited image may use acombination of fundamental and harmonic in the near field andfundamental only in the far field.

27. The rate of decorrelation can be used as an approximate estimate oftransducer speed with respect to the imaged region and can be providedas a guide to the user. The rate of decorrelation can be derived usingDoppler processors (correlators). Effectively, if a Doppler power signalis detected then there is motion. The size of this decorrelation signalis a function of speckle decorrelation.

Doppler cross-correlators can be used to correlate successive linesfired along one acoustic line direction. If the signal decorrelates bymore than certain level (i.e., maximum cross-correlation level is belowa threshold, e.g., 0.7), then this is an indication that the transducerhas been moved too fast and the system displays a warning to the user.The advantage of this approach is that it allows the already-existingprocessing power of the ultrasound machine to be used to estimatewhether the image acquisition is likely to be effective before the imageacquisition is complete and the potentially time-consuming imagetransfer process has begun.

The rate of motion can be estimated using the Color Doppler processorsalready present in most ultrasound machines. This allows an indicationto the user of poor speed control (i.e. transducer movement that is fastor out of plane) before the motion estimation process. In some cases, itis easier to do the motion estimation after complete image sequencecollection and hence there is value in having an approximate estimate orwarning of possibly non-optimal acquisition.

Effectively, the Doppler processors are used to measure pulse to pulsedecorrelation. In one embodiment, beamformed I,Q signals are applied tothe B-mode processor and to the Color Doppler processor. Generally,different acquisitions are used for the B-mode and Color Doppler signals(Doppler signals are typically more narrowband). Once the baseband,beamformed signals are filtered, Doppler processing is performed on theI and Q signals to derive Doppler-related frequency estimates resultingfrom motion. Doppler processing is well known in the art. See forexample “Real time two dimensional blood flow imaging using anautocorrelation technique,” Kasai et al. Tran. Sonics and Ultrasonics,Volume SU-32, pages 458-464 (1985). See also the detailed discussion inMaslak, U.S. Pat. No. 5,555,534, assigned to the assignee of thisinvention, the disclosure of which is incorporated herein by reference.

The Color Doppler processor typically includes low-pass filters, clutterfilters and an autocorrelator. The clutter filters are typically formedas delay line cancellers, and they are used as low frequency rejectionfilters to eliminate large echo signals from stationary and slow movingobjects which have low or zero Doppler frequency shift. Theautocorrelator autocorrelates signals from the clutter filters andproduces output signals for a Doppler Energy calculator, a DopplerVariance calculator and a Doppler Velocity calculator.

In the current application, the Doppler Energy signal is of greatestinterest. Generally, a significant Doppler Energy signal may beassociated with too rapid motion of the transducer relative to theregion being imaged and used to generate a warning to the user.Similarly, a sudden motion will cause a color flash to appear. Colorflash is well known in diagnostic ultrasound. Essentially, it is thisflash indication which is being used here to highlight a sub-optimalmotion. Since the application does not specifically require adetermination of Doppler Energy over a 2D region (except when forming anextended view image of Doppler Energy images), a very narrow examinationof the image may be made to determine if there is excessive DopplerEnergy present. Typically one scans numerous acoustic lines to map out a2D region. However, for the current application it is sufficient to fireDoppler associated acoustic lines in a single direction interleaved withthe B-mode beams used to acquire the B-mode parent image. Ideally, theselines are oriented at a non-zero angle with respect to the normal to thetransducer face. This improves the Doppler angle for azimuthal motion.(Doppler processors detect the component of motion parallel to the beamand hence it is preferable to orient the beam so that it has at leastsome component in the azimuthal direction.) By experimentation, onedetermines the correct setting for the clutter filter to remove near DCcomponents, and the Doppler Energy threshold above which the machineproduces an indication that transducer velocity may be excessive.Typically, to minimize noise one can integrate the Doppler Energysignals derived over time as the Doppler processor determines theDoppler Energy level for increasing range associated with a Doppler linefiring. (Conventionally, the Doppler Energy levels are compared to athreshold as they are acquired and if they exceed a threshold a coloredpixel is drawn corresponding to the associated position in range andazimuth on the B-mode Image.) When Doppler Energy is used fordetermining sub-optimal motion, one can tailor the clutter filtersetting and the energy threshold above which one assumes thatnon-optimal motion has occurred.

Experiments with different transducers and different frequencies can beperformed to determine the relationship between speckle decorrelation(between two line firings at a known time interval along the same lineaxis) and speed of motion in the elevation direction. Experiments canalso be performed to determine the optimal transducer speed (or maximumand maximum workable speeds) and these can be compared with thedecorrelation values for different speeds. These values are preferablystored in a look up table in the system. During operation, the systemtakes account of transducer type and frequency and estimates speed oftransducer motion from the measured line-to-line decorrelation value. Ifthis speed is too high or too low, an indication is graphicallydisplayed on the system screen. Alternatively, the estimated speed isdisplayed.

The output of the evaluation of the Doppler Energy determination can bepresented to the user in a number of ways.

(a) A binary indicator can be displayed advising the user whensub-optimal transducer motion has been detected.

(b) A numerical output indicating relative Doppler Energy level can bedisplayed—either the maximum value for a particular frame to frame caseor the mean Doppler Energy level over the entire scan.

(c) A Color Doppler Energy representation can be displayed on the screenin the conventional manner. In this case, a line-like Color Energyregion appears on the image, and the user simply observes whether itflashes.

In addition to using the Doppler power or energy signal to detect poormotion estimates by means of the flash signal, it is also possible touse the Doppler velocity estimators to estimate transducer motion.Preferably, a Doppler velocity acquisition line is oriented at anon-zero angle with respect to a line oriented perpendicular to thetransducer face (e.g. 45 degrees). When a velocity of for example 10mm/s is detected, one can infer the scanning velocity in the pureazimuthal direction since we can assume that the actual transducermotion is parallel to the azimuthal direction. The velocity estimatedfor the transducer motion after correction for the Doppler angle (45degrees) is 10/cos (45 degrees)=14.1 mm/s. This value can be output tothe display numerically or graphically. Alternatively, the detectedazimuthal velocity is compared to a threshold velocity (above whichimage alignment processing becomes problematic), and a warning ispresented to the user by visual or audible means.

Similarly, the PW or CW Spectral Doppler processor can be used toestimate the velocity of the transducer probe relative to the tissuebeing imaged (the “sweep velocity”). The velocity is derived from thefrequency component occurring with highest signal level from among thevarious frequency components examined.

In all cases, the Doppler parameters such as filter settings, pulseshapes and threshold levels are preferably optimized by modeling orexperimental techniques as is well known in the art.

FIG. 10 provides an example of a method for using Doppler signals toestimate the rate of transducer motion in an image alignment method. Asshown in FIG. 10, the first steps 50, 52 are to acquire multipleultrasound images and to align the ultrasound images. Preferably, thisalignment is performed as discussed above. However, for this aspect ofthe invention it should be understood that any suitable alignment methodcan be used, including those described in co-pending U.S. patentapplication Ser. No. 08/916,585 (U.S. Pat. No. 6,014,473) and in WengU.S. Pat. No. 5,575,286, the disclosures of which are hereinincorporated by reference.

The rate of transducer motion is estimated in step 54 from Dopplersignals associated with the images. This motion estimate can be made asdescribed above, and the motion estimate is then used in step 56 tosignal an excessive rate of transducer motion to the user. As pointedout above, this can be done by a visual or an audible alarm, or byindicating the actual estimate of transducer motion to the uservisually.

In addition or alternative to providing a user alarm as a function ofthe estimate of motion, poor correlation indicates the desire to repeatthe scan of the target as discussed above. Various measurements ofcorrelation may be used, such as the declorrelation discussed above, across-correlation or the ratio of the minimum to the average sum ofabsolute differences as discussed below. For poorly correlated componentimages, an audible or visible alarm is provided to the user. The alarmis provided regardless of whether the associated estimate of motion issatisfactory or within operational constraints or settings.

28. Pixel values can be interpolated between acquired values to yield ahigher accuracy motion estimate (effectively giving sub-original pixelresolution). This interpolation process is well known and is oftenperformed by the scan-converter. The interpolation may be linear (eitherin X or Y or both) or it may be curve-based. For example, a cubic splinecan be fit to available data. Linear interpolation is usually adequateand is often faster. In practice sub-pixel estimation using thequadratic fit technique described above is often more efficient.

29. When multiple, partially overlapping frames are combined afterregistration as described above, the combination can be performed inmany ways. For example, pixel I (i, j) of the combined image can beformed as a simple average:

I_((i,j))=(I_((i,j)) ^(Frame1)+I_((i,j)) ^(Frame2)+I_((i,j))^(Frame3)+)/3.

Alternately, non-uniform weights can be used, e.g. [0.25, 0.5, 0.25].Also, recursive compounding techniques (i.e. infinite impulse response(IIR) techniques) can be used:

I′_((i,j))=α(I_((i,j)) ^(FrameN))+(1−α) (I_((i,j)),

where IFrameN is a newly acquired frame of data, I(i, j) is thecurrently existing pixel data, and I′(i, j) is the modified pixel datathat takes into account the existing pixel data, the newly acquireddata, and α. In general, α is less than one and may be equal to 0.3, forexample. (If I(i, j) has not been created, then α=1). With thisapproach, each compounded frame overwrites or modifies thepreviously-compounded pixel values from earlier frames based on the newpixel values. The compounded image can then be optimized using histogramequalization to improve contrast or using the method described inUstuner U.S. Pat. No. 5,479,926, assigned to the assignee of the presentinvention. Alternatively, self normalizing recursive weights can beused.

I′_((I,j))=[I_((i,j)) ^(FrameN+nI) _((i,j))]/(n+1).

In this example, pixel (i,j) comprises a summation of pixels from Ncomponent images, divided by N. To obtain the above equation, alpha isset to ¹/(n+1).

As shown above in the simple average example, the images are combined inone embodiment as a function of finite impulse response (FIR) filtering.As used herein, FIR and IIR filtering comprise any device forimplementing FIR or IIR compounding techniques (i.e. implementing FIR orIIR filtering). For FIR filtering, a device provides weighted averagingof a finite number of component images (e.g. frames of data). Forexample, the combined extended field of view image is free of recursivecompounding for the overlapping region. No one of the compounded imagescomprises a previously compounded image, but the images may have beenpreviously used in other compounding.

In the three images example above, the data from each image for a pixelis multiplied by equal (e.g. 1) or unequal weights, summed and thendivided by the number of images (i.e. averaged or weighted summation).This pixel of the extended field of view image (i.e. compounded image)is calculated once by averaging all the pixels from different framescoincident at this pixel location (i.e. not recursive). FIR compoundingreduces persistence of undesired signals, such as noise, artifacts, orflash, associated with recursive compounding.

In one embodiment, the weights for FIR or IIR compounding are differentfor different portions of the component image being compounded. Forexample, the weights applied to data for a component image are differentas a function of location within the image. Side portions may beemphasized over a center portion of an image by applying greater weightsfor pixels representing the side portions and lesser weights for pixelsrepresenting the center portion. For Vector® or sector images, the sideportion decorrelate faster than center portions, so are preferablyemphasized. Various weight curves as a function of location within theimage may be used, such as experimentally determined curves depending onthe application. Alternatively, center portions or other portions of theimages may be emphasized. In one embodiment for FIR compounding, theoverlap of the images is such that the weights total a value of one foreach pixel location within the extended field of view image even withweights that vary for each component image as a function of location.

Alternatively, a portion of one or more of the component images isclipped or eliminated (e.g. a zero weight is applied to a portion of thecomponent image). Such variations as discussed above for estimatingmotion using different image portions may also be used for compoundingthe images. For example, only side portions, such as sides of images inthe direction of motion, are compounded. Where a same weight is appliedto each image, the contribution of some images is emphasized wherepixels from one of the images are not included in the average. Adifferent degree of compounding is provided. In one embodiment, thesubset of an image used for compounding comprises a block of pixel datarepresenting a thin azimuth portion and a substantially full rangeportion (e.g. 32 pixels in azimuth and 350 pixels in range from a 480 by640 pixel image).

FIG. 14 shows one embodiment of a mask for clipping component images.The mask applied for selecting or clipping the data is shaped like amilk carton. The milk carton shape is defined by a (1) top width, (2)bottom width, (3) angle of the sides and (4) height of the milk carton.Other shapes may be used, such as square, rectangular, symmetric,asymmetric, circular, oval or polygonal shapes. The milk carton shape ofFIG. 14 is oriented so that the height corresponds to the rangedimension. Other orientations may be used. The height of the milk cartonmask comprises a total depth of the component image. Heights less thanthe total depth may be used.

The mask of FIG. 14 is applied to clip data for all or a subset of thecomponent images. In one embodiment, a different mask is applied to asubset of the component images. For example, the mask shown in FIG. 14is applied to the component images compounding to form a center portionof the extended field of view image. For component images associatedwith edge or side portions of the extended field of view image, one ofthe side portions of one or more component images is selected tocomprise the side portion of the extended field of view image. For theleft side of the extended field of view image, the left portion of thecomponent images is maintained by the mask shown in FIG. 15. For theright side of the extended field of view image, the right portion of theassociated component images is maintained by the a mask, such as themirror image of the mask of FIG. 15. The mask of FIG. 15 is shaped foruse with Vector® scan formats, but other scan formats and associatedmasks may be used.

Since the component images represent different transverse locations ofthe target, fewer pixels are available to compound for the edge portionsof the extended field of view image. To avoid having the extended fieldof view image vary in appearance (e.g. generation of compounding relatedbands), the mask varies as a function of relative position of thecomponent images. For the first component image (i.e. the componentimage with the leftmost transverse location), the compounding maskcomprises the mask shown in FIG. 15.

The mask applied to adjacent component frames varies, such as by makingthe line on the left side of the mask more vertical. The angle of theline is more vertical for component images further away from the sideportion of the extended field of view image. The mask of FIG. 15 isvaried as a function of the position of the component image until themask of FIG. 14 is applied. This smooth transition between masks avoidsprocessing artifacts. The mask variation process is repeated forcomponent images associated with the other side of the extended field ofview image.

In one embodiment, one or more component images are eliminated to changethe effective weighting applied to other images. One or more pixels ofthe extended field of view image is compounded from a plurality ofcomponent images. For example, 10 component images per pixel arecompounding for most or all of the extended field of view image. Tochange the effective weighting, one or more of the component images iseliminated. Each remaining image contributes more heavily to theextended field of view image. The values of the weights applied areadjusted to sum to one for FIR compounding or sum to a value less thanone.

As an example of eliminating a component image, component images aredecimated in response to the estimated motion of successive images. Ifthe next component image is associated with a small amount of transducermovement, this component image is eliminated.

In one example, two variables, CumulativeCx and CumulativeTx, are set tozero. The motion between two component images is estimated, such asestimating azimuthal and range translations and an angle of rotationfrom the middle of a tracking block. An estimate of the azimuthaltranslation is also estimated for the top-middle portion of the trackingblock. CumulativeCx is set equal to CumulativeCx plus the x translationfrom the middle (Cx) of the tracking block, and CumulativeTx is setequal to CumulativeTx plus the x translation from the top-middle (Tx) ofthe tracking block. The maximum of the absolute value of CumulativeCxand CumulativeTx is determined. If this maximum is above a threshold,the component image is included in the extended field of view image andCumulativeCx and CumulativeTx are reset to 0.0. If the maximum is belowthe threshold, the associated component image is not included in theextended field of view image. This process is repeated for each possiblecomponent image. Translation in range may additionally or alternativelybe examined to determine whether to eliminate a possible componentimage.

As discussed above, OpenGL software and/or hardware is used in oneembodiment to provide low cost and efficient weighted summationcompounding (FIR filter). A single image buffer configured to include anopacity channel and color channels accumulates alpha blended values.Where a personal computer, motherboard, off-line processor or extraprocessor within a system performs calculations for the extended fieldof view image, OpenGL hardware and software are cheaply implemented. Forexample, see U.S. Pat. No. 6,159,150, the disclosure of which isincorporated herein by reference, which discloses integrating aPerspective™ or other computer within an ultrasound system housing.Substantially real-time or quick compounding is provided withcommercially available OpenGL accelerator cards. Alpha blending avoidsthe need for prior input regarding the number of component images to becompounded. FIR compounding is provided without substantial memoryrequired to separately store each of the component frames prior tocompounding.

For alpha blending with OpenGL, the color of a source pixel, representedas an R_(s) G_(s) B_(s) value, is combined with the color of adestination pixel. The source pixel comprises a pixel from a componentimage, and the destination pixel represents a pixel from anothercomponent image or a pixel that is the partial sum of a plurality ofother component images. The destination pixel comprises the R_(D) G_(D)B_(D) values stored in the image buffer. In addition to RGB values, thesource and destination are further represented by alpha or opacityvalues, A_(s) and A_(D).

To compound the source pixel with the destination pixel, source anddestination factors are specified. The factors comprises a pair of RGBAquadruplets (S_(R) S_(G) S_(B) S_(A) and D_(R) D_(G) D_(B) D_(A)) to bemultiplied by respective components of the source and destinationpixels. This blending is represented by:

(R_(S)S_(R)+R_(D)D_(R), G_(S)S_(G)+G_(D)D_(G), B_(S)S_(B)+B_(D)D_(B),A_(S)S_(A)+A_(D)D_(A)).

Each component of this combined quadruplet is clamped to [0,1].

As a default, OpenGL combines the source and destination pixels byadding the component parts (see pages 214-219 of Open GL ProgrammingGuide, Second Edition, Woo et al., ISBN 0-201-46138-2). The quadrupletfactors are set to one as the default. Alpha blending provided by OpenGLprocessing performs equal or unequal weighting of the component images.The weighting is set by selecting values for S_(R) S_(G) S_(B) S_(A) andD_(R) D_(G) D_(B) D_(A).

For equal weighting, the components of each of these quadruplets is setto be equal, such as set to one or set to a specific alpha value. Forexample, S_(R) S_(G) S_(B) S_(A)=1, 1, 1, 1 and D_(R) D_(G) D_(B)D_(A)=1, 1, 1, 1. As another example, S_(R) S_(G) S_(B) S_(A)=a_(r),a_(g), a_(b), a_(a) and D_(R) D_(G) D_(B) D_(A)=1, 1, 1, 1.

For compounding, the initial framebuffer or destination values are setto 0. The source pixel of each component image is iteratively blendedwith the destination values. For example, let S_(R) S_(G) S_(B) S_(A)=1,1, 1, 1 and D_(R) D_(G) D_(B) D_(A)=1, 1, 1, 1 where N component imagesare compounded. The opacity of each source pixel is set to 1 in oneexample, but other values may be used. A table representing the R (red)and A (alpha) channels of the destination is shown below:

TABLE 1 R_(d) (Red Channel in the A_(d) (Alpha Channel in the Iframebuffer) framebuffer) 0 0 0 1 R_(S) ⁽¹⁾ + 0 1² + 0 2 R_(S) ⁽²⁾ +R_(S) ⁽¹⁾ 1² + 1² 3 R_(S) ⁽³⁾ + (R_(S) ⁽²⁾ + R_(S) ⁽¹⁾) 1² + (1² + 1²) 4R_(S) ⁽⁴⁾ + (R_(S) ⁽³⁾ + (R_(S) ⁽²⁾ + R_(S) ⁽¹⁾)) 1² + (1² + (1² + 1²))N $\sum\limits_{i = 1}^{n}\quad R_{s}^{(i)}$

n1²

This blending for the R channel is represented mathematically as:$R_{c} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}R_{s}^{(i)}}}$

Substituting for destination quantities, the blending functionsimplifies to:$R_{c} = {{\left( \frac{1}{n} \right)\quad \left( {\sum\limits_{i - 1}^{n}R_{s}^{(i)}} \right)} = \frac{R_{d}}{A_{d}}}$

To obtain the equal weighted compound value for the extended field ofview image, the RGB values of the destination are divided by thedestination alpha value.

Where the source factor quadruplet S_(R) S_(G) S_(B) S_(A) are set toa_(r), a_(g), a_(b), a_(a) where a is not equal to one or to scale theresult, the RGB values as calculated above are multiplied by therespective “a” value or scale value (e.g. R_(x)=aR_(d)/A_(d)). To avoidclamping where a is not equal to one, the value of “a” is small, such as0.05 for compounding 20 source pixels (i.e. 20 component imagesoverlapping at that pixel).

For unequal weighting, the components of each of these quadruplets areset to be unequal. Where the S_(R) S_(G) S_(B) S_(A) and D_(R) D_(G)D_(B) D_(A) values are arbitrary or unequal, the destination pixel isrepresented mathematically as:$R_{d} = \frac{\sum\limits_{i = 1}^{n}{R_{s}^{(i)}a_{i}}}{\sum\limits_{(s)}^{n}a_{i}}$

Where α_(i) is the weighting at that pixel from image i. Before alphablending as discussed above, each pixel is converted as a function ofthe pixel's alpha value α_(i). For example, R_(s) is converted to{square root over (α_(i)+L )} R_(s). In one example, S_(R), S_(G),S_(B), S_(A) is set as {square root over (α_(i)+L )}, {square root over(α_(i)+L )}, {square root over (α_(i)+L )}, {square root over (α_(i)+L)}, and D_(R), D_(G), D_(B), D_(A) is set as 1, 1, 1, 1. The table belowrepresents alpha blending the red and alpha channels using the convertedpixel information:

TABLE 2 I R_(d) (Red Channel in the framebuffer) A_(d) (Alpha Channel inthe framebuffer) 0 0 0 1 R_(S) ⁽¹⁾ {square root over (α₁ +L ·)} {squareroot over (α₁+L )} + 0 {square root over (α₁ +L ·)} {square root over(α₁ +L + )}0 2 R_(S) ⁽²⁾α₂ + R_(S) ⁽¹⁾α₁ α₂ + α₁ 3 R_(S) ⁽³⁾α₃ + (R_(S)⁽²⁾α₂ + R_(S) ⁽¹⁾α₁) α₃ + (α₂ + α₁) 4 R_(S) ⁽⁴⁾α₄ + (R_(S) ⁽³⁾α₃ +(R_(S) ⁽²⁾α₂ + R_(S) ⁽¹⁾α₁)) α_(S) + (α₃ + (α₂ + α₁)) N$\sum\limits_{i = 1}^{n}\quad {R_{s}^{(i)}\alpha_{i}}$

$\sum\limits_{(s)}^{n}\quad \alpha_{i}$

Substituting for calculated quantities, the blending function simplifiesto: $R_{c} = \frac{R_{d}}{A_{d}}$

To obtain the unequal weighted compound value for the extended field ofview image, the source pixel values are converted as a function ofrespective weights, and the RGB values of the destination are divided bythe destination alpha value.

In one embodiment, the pixel data is scaled down to avoid saturation ofthe frame or destination buffer. For example, each R, G or B value iswithin the range of 0 to 255, and the frame buffer is also 8 bits perchannel. If the R, G or B values are large and/or the number ofcomponent images is large, the frame buffer may saturate.

To avoid saturation, only the higher order N bits (downshifted by M-Nbits where M is the depth of each color channel (for example, M=8)) ofthe RGB channels are used in the component images during compounding.For example, if only the highest 4 bits are used, the scaled downcomponent pixel data range from 0 to 15. Other numbers of bits, forexample, from 2 to 7, may also be used. Each overlapping pixels of thesecomponent images are then accumulated and multiplied by the quantity, q,given by:

q=2^(m−1)/2^(n−1)/α

where α is the residual value in the alpha channel at the pixel.Although the information content is reduced in the component images dueto scaling down, dithering during compounding component images restoressome or all of the lost information in the final compounded image.

In addition to color saturation, the alpha channel may also saturate ifthe number of component images is sufficiently large. This situation isunlikely in most practical cases.

In another embodiment, the accumulation buffers defined in OpenGL areused for compounding. Since these buffers are typically wider (forexample 16 bits) than the color buffers (for example 8 bits), saturationartifacts are less likely in the final compounded image.

To further avoid saturation of any channel, the number of componentimages used for compounding is controlled as discussed above. SinceOpenGL alpha blending operates without a previous knowledge of thenumber of frames to be compounded, decimation of component images isimplemented without adverse effects on compounding. Masking or clippingportions of component images as discussed above also reduces the amountof information at a pixel to avoid saturation.

In one embodiment for compounding with OpenGL alpha blending, aplurality of variables are controlled to avoid saturation and togenerate an clinically acceptable extended field of view image. Forexample, a number of bits to downshift, a top width of a mask, a bottomwidth of the mask, an angle of the mask, a number of component imagesused for compounding the ends of the final compounded image, and athreshold amount of motion used for automatically skipping oreliminating a possible component image are selected. In one embodiment,the threshold used for eliminating possible component images is selectedas a fraction of the top width of the mask, such as 0.5 of the width.Other variable may be used, such as any of the variable discussedherein. The optimum variables are determined as a function of theclinical application and system. Multiple settings for any giveapplication and system may be provided for user selection. The set ofparameters is independent of the probe geometry and type, but mayaccount for these factors. Other techniques and Application ProgrammingInterfaces that support alpha blending may also be used.

Characteristics of the component images may be optimized for compoundingthrough variation. One component image has different characteristicsthan another component image. For example, two component images arecharacterized by different acquisition frequencies. Acquisitionfrequencies include transmit frequencies, receive frequencies andcombinations thereof. Compounding component images associated withdifferent acquisition frequencies reduces the effects of speckle. Othercharacteristics for variation include aperture size (transmit orreceive), filter settings (transmit or receive), focusing parameters orother variables controlled by the acquisition ultrasound system or apost-acquisition system.

The variation is repetitive in one embodiment. Every other componentimage is similar, so that two different types of component images areprovided. Other cycles of repetition or number of types of componentimages may be used, such as every third or fourth component image issimilar in characteristics. A randomized process may be used.

In one embodiment, motion is estimated between images with similarcharacteristics. The speckle patterns of such images is likely morecorrelated for more accurate motion estimation. Motion is estimated forother component images by interpolation, curve fitting, or separately.Alternatively, motion is estimated using images not compounded to formthe extended field of view image.

The same or different component images may be processed differently forcompounding than for estimating motion. For example, two componentimages are selected. Motion is estimated between the two selectedcomponent images without any, without further or with differentfiltering. The two component images are filtered, such as low passspatial filtering, for compounding. Other types of filtering, such ashigh pass or band pass filtering, or smoothing processes, such ashistogram equalization or other gray scale mapping modifications (e.g.changing the shape of the gray scale curve), may be used. The filteringis applied on line data (one dimension) or the image data (twodimensions). Other smoothing or filtering processes may be used, such asdisclosed in U.S. Pat. No. 5,479,926, the disclosure of which is hereinincorporated by reference. In one embodiment, the filtering or otherprocessing highlights specular reflectors or other image attributes. Inother embodiments, the component images are filtered for estimatingmotion, but not filtered or filtered differently for compounding, asdiscussed above.

As also discussed above and in U.S. patent application Ser. No.09/384,707, the disclosure of which is incorporated herein by reference,one or more of the component images are transformed or scaled tocompensate for scanning rate error. Scanning rate error compensationspatially corrects the component images and the amount of detected frameto frame motion to account for transducer motion while image data isacquired for a single scan. This correction avoids inaccurate motionestimation and resulting compounding of improperly aligned data. Bycompensating for the scanning rate as a function of the frame rate, theextended field of view image more accurately represents the scannedtarget.

The FIR or IIR compounding may be adaptive. Adaptive compoundingcomprises changing a characteristic of the compounding in response toone or more features. For example, (1) the width or other dimension of acomponent image used for compounding (e.g. the mask), (2) the weight orweights, (3) the type of compounding (e.g. IIR, FIR or no compounding),or (4) other compounding characteristic is changed in response to (a) anestimation or estimations of motion between two or more componentimages, (b) a correlation or correlations between two or more componentimages, (c) image pixel data, (d) a location of the pixel within thecomponent image, (e) other calculated features or (f) combinationsthereof.

The mask applied for compounding is adaptive in one embodiment. Asdiscussed above for compounding, a portion of a component image iscompounded. The portion of the image is selected as discussed above forcompounding or for selecting a tracking block.

The width, height, size, shape or combinations thereof of the portion ormask is determined in response to an estimate of motion. The imagetexture width (e.g. for azimuthal translation) is set so that a setnumber of component images are compounded for a given pixel. Forexample, if the estimated motion is 10 pixels to the right azimuthallyand 10 component images are to be compounded, a 100 pixel wide portionof the component images is compounded. For any given azimuthal pixellocation, 10 component images are compounded. The 10 right most lines ofpixel data from the first frame overlap with the 10 left most lines ofpixel data from the tenth frame. Where the estimated motion varies, themask is varied to account for the changing motion. The actual number ofcomponent images compounded may vary from the set number, such as 10.Additionally or alternatively, the number of images for compounding isset by eliminating component images.

As another alternative or additionally, the weights or opacity used forIIR or FIR compounding may adapt in response to estimated motion orcorrelation between two or more component images. The varied weightseffectively reduce the influence of some component images, resulting inan extended field of view image that is similar to an image formed bycompounding a set number of component images. The same weight is usedfor each pixel of the component image or varies for different pixels ofthe component image. The image texture width or mask is fixed or alsovaries as discussed above.

For IIR compounding, the recursive weight or opacity is adapted as afunction of the estimated motion. A table or other listing of possibleweights as a function of the estimated motion is determined based on thesystem and application. Typically, a high motion estimate is associatedwith a higher weight, α, applied to the most recent component image andwith a lower weight (i.e. 1−α) applied to the previously compositedimage. Where the estimate of motion varies between component images, theweights are varied correspondingly. In other embodiments, FIRcompounding weights adapt to the estimates of motion.

The IIR weights or FIR weights may adapt as a function of the locationof the pixel being compounded within the component image or the extendedfield of view image. For example and as discussed above, lesser weightsare applied to pixels associated with azimuthally center portions of animage.

The weights may also adapt as a function of a combination of thelocation of the pixel being compounded and a correlation or estimate ofmotion. Local estimates of motion or correlations are calculated. Theweighting applied for that local area varies as a function of the localestimate of motion or correlation.

Varying weights as a function of the correlation between images avoidsblurring the extended field of view image. Correlation indicates thequality of a match between aligned images. The correlation may bedetermined as a function of unaligned images.

If the component images poorly correlate, less compounding or moreunequal weights are applied. In the extreme, the pixel data isoverwritten rather than compounded. If the component images have a highcorrelation, more equal weighting is applied. For example, if thenormalized cross-correlation coefficient is 0.5, the opacity (i.e. IIRor FIR weight) is set to 0.6, and if the normalized cross-correlationcoefficient is 0.9, the opacity is set to 0.1. Other values may be used,such as values representing a curve of opacity to cross-correlationcoefficient determined empirically.

Compounding as a function of correlation is independent of compoundingas a function of estimated motion. The correlation value used comprisesa cross-correlation value, an inverse of a decorrelation value, aminimum sum of absolute differences or other value indicating asimilarity between component images. For example, the minimum sum ofabsolute differences is compared to the average sum of absolutedifferences. This comparison is less computationally intensive tocalculate than a cross-correlation, especially where the minimum sum ofabsolute differences is calculated to estimate motion. A curve or tableof weights as a function of a ratio or other function relating theminimum to average sum of absolute differences is determinedempirically. For example, a ratio of 0.5 is associated with an IIRopacity or alpha value for OpenGL FIR compounding of 0.4, and a ratio of0.8 is associated with an IIR opacity of 0.8. Other values may be used.In one embodiment, correlation, such as speckle decorrelation, is usedto estimate out-of-plane movement (elevational movement) and also usedto adapt the degree of compounding.

As an alternative to adapting the weight or weights as a function ofcorrelation, the number of component images compounded is adapted as afunction of correlation. Varying the number of component imagescompounded effectively varies the weights. For a high rate ofdecorrelation, fewer images are compounded together. Alternatively, themask size, shape or application is varied as a function of thecorrelation.

As discussed above, characteristics of the compounding may adapt as afunction of a change in the correlation. The magnitude of the changedetermines the weights, image mask or type of compounding.

In one embodiment, the type of compounding is adapted to one or more ofthe features discussed herein. For example, one of IIR, FIR or nocompounding is used for one or more pixels in response to correlation.

Other processes may be adaptive. For example, the selection of thecomponent images is adaptive. The frequency selection of possiblecomponent images within a sequence adapts as a function of estimatedmotion. For example, every other or every third possible component imageis selected where the amount of motion is low. Possible component imagesare more frequently selected where the amount of motion is high. Asanother example, the frequency of the selection of possible componentimages decreases for high correlation between the component images.Highly correlated component images, such as a minimum to average sum ofabsolute differences ratio less than 0.1 or a cross-correlationcoefficient higher than 0.9, provide less speckle reduction throughcompounding, so images less likely highly correlated (e.g. every other,third or fourth component image) are selected. The selected images arethen compounded or adaptively compounded as discussed above. In oneembodiment, the compounded extended field of view image is processedafter compounding but prior to generation of a responsive display. Forexample, histogram equalization, high pass filtering, low passfiltering, adaptive filtering (e.g. filtering as disclosed in U.S. Pat.No. 5,478,926), gray scale curve remapping or other signal processing isperformed on the compounded extended field of view image.

30. FIG. 13 provides a flow chart of an imaging method that utilizes theimage acquisition, alignment, and composition techniques described aboveto create an extended longitudinal section of a tubular organ such as ablood vessel. In step 70 a catheter-mounted probe is introduced into atubular organ such as a vessel, typically by inserting the probe into alumen of the vessel. For example, the probe can include an ultrasonictransducer array mounted on a catheter for insertion into a human veinor artery. In step 72 ultrasound images are acquired as the probe ismoved along the vessel, either into or out of the body of the subject.In step 72 the images are acquired in respective image planes that areoriented generally parallel to the path along which the probe is moved.In step 74 the images acquired in step 72 are aligned, using any of thealignment techniques now known or later developed by those skilled inthe art. In step 76 the aligned images are composited, and in step 78the extended field of view image as composited in step 76 is displayed.The composited image of step 76 is in this example an extendedlongitudinal sectional view of the vessel.

The techniques described above allow for viewing of long sections ofvessels and surrounding anatomy and also allow for viewing landmarktissue to allow the user to orient a region of interest, which may be asmall area in many situations, in a context of a larger image includingsuch landmark tissue. Examples of suitable catheter-mounted transducerprobes are described in U.S. Pat. Ser. No. 5,846,205 and Ser. No.08/802,621, both assigned to the assignee of the present invention andincorporated by reference herein.

31. When an extended view image is being acquired using a low frame rateand a relatively high manual transducer sweep rate across the tissuesurface, a dimensional error is encountered. This is because theultrasound system typically scans the array from one side to the otherside of the transducer array. If the transducer array is 50 mm wide, theframe rate is 10 frames per second, and the transducer is scanned at arate of 5 mm/s, then in the time it takes to scan one frame (0.1 s) thetransducer has moved 0.5 mm. When the transducer array is scanned in onedirection this error corresponds to a 0.5 mm image extension and whenscanned in the other direction it corresponds to a 0.5 mm imagecontraction in the azimuthal motion. (This is a 1% dimensional error.)This error scales with scan rate and inversely with frame rate. Duringextended field imaging, we can determine the image motion and hence itis possible to correct for this effect.

Consider the case where the system scans from transducer element #1 (atone end of the array) to transducer element #N (at the other end of thearray). If the motion estimator detects a motion between two frames of 1mm in the direction of element 1, then the real image region which hasbeen scanned has contracted by 1 mm. Hence we must scale the width ofthe region by a scale factor K, where

K=1−(1 mm/Image width(mm)),

K=1−{fraction (1/50)}=0.98 for a 50 mm wide image.

Similarly, for motion in the other direction, the scale factor K is setequal to 1+{fraction (1/50)} to extend the width of the imaged region.The motion estimate (translation) is preferably corrected by the samefactor, i.e. the estimated translation is multiplied by 0.98 in theabove example for transducer motion in the direction of element #1. Seethe discussion of image correction in U.S. Pat. No. 5,873,830, assignedto the assignee of the present invention and hereby incorporated byreference herein.

The motion of the probe also affects the determination of rotation, andestimates of rotation should also be modified. Typically, the angularestimate is derived from the angle associated with the relative motionof pixels in the azimuthal direction at the top and bottom of the searchblock. This angle must now be corrected to take account of the fact thatthe X dimensions of the pixels are different from that originallyassumed. For the small angles of interest here, this can be effectedapproximately by multiplying the angles produced before trying todetermine the transducer probe velocity error by the factor determinedabove for the correction to the image azimuthal dimension and motionestimate, i.e. the estimated rotation is multiplied by 0.98 in theexample above for transducer motion toward transducer element #1.

The simplest way to implement this correction is to modify the texturemapping width when doing the image compositing using OpenGL texturemapping.

32. When compositing an image including Color Doppler data (velocity orenergy), it is preferable to acquire separate B-mode and Color regionsas is conventionally done inside the system. Typically, an ultrasoundmachine acquires a complete frame of B-mode and a partial frame ofColor. Depending on the level of Color Energy and/or Velocity,particular pixels are overwritten or mixed with color values. Colorregions do not in themselves contain the speckle data used in motionestimation of the type described above. Therefore, it is preferable touse the raw B-mode images for motion estimation prior to the Colorsuperimposing step.

On occasion, only the color superimposed images are available forestimating transducer motion. There are a number of methods that can beused, as follows:

(a) The Color pan boxes can be restricted in size to a small part of theparent image. The color pixel data, which contains no speckle motiondata, does not corrupt the motion estimate to any significant extent.

(b) A color superimposing step can be used which mixes Doppler Color andB-mode speckle. Thereafter, the B-mode speckle is processed by referringto the Color lookup table and inferring the underlying B-mode value. Ifnecessary, the B-mode values are rescaled if they were scaled during thecolor mapping process.

(c) The Doppler Color regions can be restricted to specified regions ofthe parent image. Typically only a portion of the parent image isDoppler processed since Doppler processing is associated with slow dataacquisition and associated slow frame rates. Preferably, the Color panbox is limited to only a portion of the available image, and preferablythe Color pan box is prohibited from the center of the test block whichis being used for motion estimation. Although this may sound undulyburdensome, it is not necessary that the majority of the image be incolor since only small portions of the image are used in composition toform an extended field of view. If these small regions are colorregions, then an extended color image will still result. This assumesthat the composited regions are different from the region used formotion estimation. In this process, motion is estimated for the B-moderegion. One then takes account of the difference between the center ofthe motion estimation block and the center of the color compositedregion during image composition. The simplest way to do this is todefine the composited region to include the center of the motionestimation region but to define that the portion of the imagecomposition region outside that containing color as completelytransparent. OpenGL™ texture mapping allows for controlled transparencyand hence this technique is easily implemented.

(d) The Color pan box region alternately can be allowed to occuranywhere in the frame as long as it does not occupy the entire frame.Prior to motion estimation, the program uses knowledge of the locationof the color regions (which may be passed to it from the imageacquisition controller of the ultrasound machine), and adaptively altersthe position of the motion estimation block to position it on one orother side of the Color region. In this way, the region for the motionestimation process is spaced from the Doppler Color region of the parentimage. Moving the Color box during acquisition can be prohibited, and tosimplify implementation, this adaptive step is performed only at thebeginning of the process.

FIG. 11 shows one preferred relative arrangement of regions within aparent image. In this case, the parent image 60 includes two B-moderegions 62, 64 and a Color Doppler region 66. The B-mode regions 62, 64are well suited for motion estimation using any of the techniquesdescribed above, and the relative positions of the B-mode regions 62, 64and the color region 66 are well known. As shown in FIG. 12, when twoparent images 60, 60′ of the type shown in FIG. 11 are composited, theB-mode regions 62, 62′ can be used for motion estimation and alignmentof the two parent images 60, 60′, while the color regions 66, 66′ can beused for extending the field of view.

33. Typically, the test block is smaller than the entire parent image,but it is also possible to use the entire image as the test block. Inthis case, it is preferred to account for the fact that there isincomplete matching data for any non-zero motion (i.e. if the framesmove from left to right, then one frame will lack data for comparison onthe left hand side and the other will lack data on the right hand side).In the MSAD calculation (or cross correlation), it is important thatvalid data be present. Therefore, when using a full frame test block,one preferably suppresses SAD calculations for invalid regions. However,in order to calculate the minimum SAD, one preferably uses some form ofnormalization to take account of the fact that some comparisons use morepixels (since they are less overlapping). One approach is to replace thesearch for minimum SAD with a search for minimum normalized SAD, wherethe normalized SAD operation normalizes the SAD by dividing the SAD bythe number of pixel-to-pixel comparisons being used.

As an example, consider a full image block of 100 by 100 pixels, wherethe search is ±3 pixels (left and right).

When calculating the SAD with a shift of three pixels to left, the threepixels on the left are invalid (not defined) on one frame, and threepixels on the right are invalid (not defined) on other frame. Therefore,the number of comparisons is (100−3−3)*100=9400. In this case, thenormalized SAD is equal to the SAD divided by 9400.

When calculating the SAD with a zero pixel shift between the testblocks, no pixels on the left are invalid (not defined) on one frame andno pixels on the right are invalid (not defined) on the other frame.Therefore, the number of comparisons is (100−0−0)*100=10,000. In thiscase, the normalized SAD is equal to SAD divided by 10,000. Of course, afull-frame test block may be sub-sampled as described above, e.g.decimated.

34. If image acquisition is not ECG gated (as is most often the case),then it is preferable to take steps to minimize perceived pulsatility inthe resulting color Doppler extended field of view image. (Pulsatilitywill appear as a color region with rapidly changing width as a functionof azimuthal position and the respective component image). One approachis to persist the Color Doppler data by separating it from theunderlying B-mode and then persisting it during the compositing step. Ifthe Color Doppler data is not available independently from the B-modedata from the ultrasound image processor, then the Color data can beseparated from the combined Color+Gray scale (B-mode) data by detectingregions in the images in which R, G and B are not balanced (RGBbalanced=gray). These regions (identified as Color) are then persistedsuch that the maximum Color value detected for any overlapping regionduring composition overwrites any previous Color value or the Gray valueif no Color value already exists at that point. Alternatively, the Colordata is separated into a separate Color image zone comprising Colorvalues and a high opacity (possibly opacity=1), and non Color values(everywhere else) with opacity=0. During OpenGL texture mapping, theColor regions will overwrite such that the peak flow condition for anyparticular region will dominate and perceived pulsatility is suppressed.When using this technique, it is preferable that the Color map used iscontinuous and exhibits a monotonic type variation for increasing ColorDoppler Energy or Color Doppler Unsigned Velocity. (If signed velocityis used then dramatically changing colors corresponding to changes inflow direction may result in an undesirable image with blotches ofdifferent color.) Other embodiments for separating Color data aredisclosed in U.S. Pat. No. 6,190,321 (U.S. Ser. No. 09/370,060, filedAug. 6, 1999), the disclosure of which is incorporated herein byreference.

CONCLUSION

The improvements and modifications described in sections 1-34 above canbe used with any suitable algorithm for registering and/or compoundingtwo or more substantially coplanar ultrasound images, such as taught inU.S. Pat. Nos. 6,014,473, 5,575,286, 5,566,674, U.S. Ser. No. 09/384,707and U.S. Pat. No. 6,159,152, the disclosures of which are hereinincorporated by reference. The embodiments described above may beimplemented on an ultrasound imaging system or on an offline processor.Various preset selections of appropriately or experimentally determinedgroups of compounding parameters, such as the types of compoundingand/or variables discussed herein, are provided to the user. Forexample, different mask shapes, mask sizes and degrees of compoundingare provided to the user for each of a plurality of selectable clinicalapplications or studies.

Once an extended field of view image has been formed as described above,it can be manipulated in well known ways, as for example by zooming orrotating using OpenGL. All of the techniques described herein may beused with tracking image frames of the type discussed in U.S. Pat. No.6,014,473. The foregoing detailed description has described only a fewof the many forms that this invention can take. For this reason, thisdetailed description is intended by way of illustration and not by wayof limitation. It is the following claims, including all equivalents,that are intended to define the scope of this invention.

What is claimed is:
 1. A medical diagnostic ultrasound method forforming an extended field of view of a target, the method comprising theacts of: (a) selecting first and second medical ultrasonic images, thefirst and second images partially overlapping; (b) compounding the firstimage with the second image in an overlapping region in response to afinite impulse response function; and (c) generating an extended fieldof view image responsive to (b); wherein at least a portion of theextended field of view image corresponding to the overlapping region isfree of recursive compounding.
 2. A medical diagnostic ultrasound methodfor forming an extended field of view of a target, the method comprisingthe acts of: (a) selecting at least three medical ultrasonic images, thefirst and second images partially overlapping; and (b) compounding theat least three images in an overlapping region in response to a finiteimpulse response function.
 3. The method of claims 1 or 2 wherein (b)comprises alpha blending.
 4. The method of claim 3 wherein (b) comprisesusing OpenGL commands.
 5. The method of claims 1 or 2 wherein (b)comprises averaging.
 6. The method of claim 5 wherein (b) comprisesaveraging with non-uniform weights.
 7. The method of claims 1 or 2wherein (b) comprises applying weights to the images, the weights largerfor portions of the images spaced from the center than for centerportions.
 8. The method of claims 1 or 2 wherein (b) comprisescompounding side portions of the images without compounding centerportions.
 9. The method of claims 1 or 2 wherein (a) comprises selectingimages responsive to different frequencies.
 10. The method of claims 2further comprising: (c) generating an extended field of view imageresponsive to (b).
 11. The method of claims 1 or 10 further comprising:(d) signal processing the compounded information prior to (c); whereinthe extended field of view image is responsive to the signal processing.12. The method of claims 1 or 2 wherein (b) is adaptive as a function ofcorrelation.
 13. The method of claims 1 or 2 further comprisingcompensating for scanning rate error.
 14. A medical diagnosticultrasound method for forming an extended field of view of a target, themethod comprising the acts of: (a) selecting first and second medicalultrasonic images, the first and second images partially overlapping;and (b) alpha blending the first image with the second image in anoverlapping region.
 15. The method of claim 14 wherein (b) comprisesusing OpenGL commands.
 16. The method of claim 14 further comprising:(c) performing (b) with an OpenGL accelerator.
 17. The method of claim14 wherein (b) comprises blending image data as a function of an alphavalue.
 18. The method of claim 17 further comprising: (c) blending alphavalues for each frame of image data; and (d) dividing the blended imagedata of (b) with the blended alpha values of (c).
 19. The method ofclaim 18 further comprising: (e) multiplying the output of (d) by avalue.
 20. The method of claim 14 further comprising: (c) using asub-set of bits comprising image data for (b), the sub-set comprisinghigher bits.
 21. The method of claim 14 wherein (a) comprises selectingimages as a function of an estimate of motion.
 22. The method of claim14 further comprising: (c) masking the first and second images; wherein(b) is responsive to the masked first and second images.
 23. The methodof claim 22 further comprising: (d) applying a different mask to thefirst image than to the second image.
 24. The method of claim 23 wherein(d) comprises varying the mask as a function of a image position withinan extended field of view image. frame rate.
 25. The method of claim 1or 2 wherein the first and second images comprise coherently adjacentline data.
 26. A medical diagnostic ultrasound method for forming anextended field of view of a target, the method comprising the acts of:(a) selecting first and second medical ultrasonic images, the first andsecond images partially overlapping and comprising coherently adjacentline data; (b) compounding the first image with the second image in anoverlapping region; and (c) generating an extended field of view imageresponsive to (b).
 27. The method of claim 14 further comprisingproviding a plurality of user selectable applications wherein (b) isresponsive to user selection of one of the selectable applications.